{"title":"Wealth survey calibration using income tax data","authors":"Daniel Kolář","doi":"10.1007/s10797-024-09849-6","DOIUrl":null,"url":null,"abstract":"<p>Wealth surveys tend to underestimate wealth concentration at the top due to the “missing rich” problem. I propose a new way of improving the credibility of wealth surveys by making them consistent with tabulated income tax data. This is possible with the harmonized triannual Household Finance and Consumption Survey (HFCS), which collects data on both income and wealth. I achieve consistency by calibrating survey <i>weights</i> using the income part of HFCS. I apply the calibration method of Blanchet et al. (J Econ Inequal 20(1):119–150, 2022b) in a new context and propose a new, intuitive way to determine the merging point where the calibration starts. I then use the calibrated weights with HFCS wealth values. Tested on Austria, calibration aligns the survey totals closer to the National Accounts, with wealth inequality increasing in the second and third survey waves. I also find a strong downward bias in the Austrian HFCS income distribution. Following the calibration, I test other top tail adjustments: replacing the survey top tail with a Pareto distribution and combining the data with a magazine rich list.</p>","PeriodicalId":47518,"journal":{"name":"International Tax and Public Finance","volume":"76 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Tax and Public Finance","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s10797-024-09849-6","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Wealth surveys tend to underestimate wealth concentration at the top due to the “missing rich” problem. I propose a new way of improving the credibility of wealth surveys by making them consistent with tabulated income tax data. This is possible with the harmonized triannual Household Finance and Consumption Survey (HFCS), which collects data on both income and wealth. I achieve consistency by calibrating survey weights using the income part of HFCS. I apply the calibration method of Blanchet et al. (J Econ Inequal 20(1):119–150, 2022b) in a new context and propose a new, intuitive way to determine the merging point where the calibration starts. I then use the calibrated weights with HFCS wealth values. Tested on Austria, calibration aligns the survey totals closer to the National Accounts, with wealth inequality increasing in the second and third survey waves. I also find a strong downward bias in the Austrian HFCS income distribution. Following the calibration, I test other top tail adjustments: replacing the survey top tail with a Pareto distribution and combining the data with a magazine rich list.
期刊介绍:
INTERNATIONAL TAX AND PUBLIC FINANCE publishes outstanding original research, both theoretical and empirical, in all areas of public economics. While the journal has a historical strength in open economy, international, and interjurisdictional issues, we actively encourage high-quality submissions from the breadth of public economics.The special Policy Watch section is designed to facilitate communication between the academic and public policy spheres. This section includes timely, policy-oriented discussions. The goal is to provide a two-way forum in which academic researchers gain insight into current policy priorities and policy-makers can access academic advances in a practical way. INTERNATIONAL TAX AND PUBLIC FINANCE is peer reviewed and published in one volume per year, consisting of six issues, one of which contains papers presented at the annual congress of the International Institute of Public Finance (refereed in the usual way). Officially cited as: Int Tax Public Finance